Although artificial intelligence (AI) and machine learning (ML) can be deployed to improve
cyber security management, not all managers understand the different types of AI/ML and …

Nanomaterials (NMs) have developed quickly and cover various fields, but research on
nanotechnology and NMs largely relies on costly experiments or complex calculations (eg …

We apply deep-neural-network-based techniques to quantum state classification and
reconstruction. Our methods demonstrate high classification accuracies and reconstruction …

We demonstrate that isomorphically map** gray-level medical image matrices onto
energy spaces underlying the framework of fast data density functional transform (fDDFT) …

In-sensor computing has gained attention as a solution to overcome the von Neumann
computing bottlenecks inherent in conventional sensory systems. This attention is due to the …

Abstract Restricted Boltzmann Machines (RBMs) offer a versatile architecture for
unsupervised machine learning that can in principle approximate any target probability …

Many optimization problems can be cast into the maximum satisfiability (MAX-SAT) form,
and many solvers have been developed for tackling such problems. To evaluate a MAX-SAT …

Extraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them
a crucial building block for the development of next-generation soft robotic systems and …

Digital memcomputing machines (DMMs) are a novel, non-Turing class of machines
designed to solve combinatorial optimization problems. They can be physically realized with …

The deep extension of the restricted Boltzmann machine (RBM), known as the deep
Boltzmann machine (DBM), is an expressive family of machine learning models which can …